Synonyms
HMM
Definition
A hidden Markov model is a twofold stochastic process composed of a first-order Markov chain, which is a finite-state machine ruled by state transition probabilities that solely depend on the immediate predecessor, and an associated probabilistic function. In a regular Markov model, the state is visible to any observer external to the model, whereas in a hidden Markov model (HMM), the state is not observable, that is, hidden, but each state has associated output probabilities over the possible observable tokens.
Introduction
Hidden Markov models (HMMs) [1] were introduced by L.E. Baum in the late 1960s [2], and since the mid-1970s [3–5], they have become popular to model the statistical variation of the spectral features in speech recognition research. In the late 1980s, HMMs were applied to the analysis of DNA and other biological sequences. Nowadays, they are ubiquitous in bioinformatics and, in particular, in biometrics.
Architecture and Types
An HMM is...
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References
L.R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)
L.E. Baum, J.A. Egon, An inequality with applications to statistical estimation for probabilistic functions of a markov process and to a model for ecology. Bull. Am. Meteorol. Soc. 73, 360–363 (1967)
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L.E. Baum, An inequality and associated maximization technique in statistical estimation of probabilistic functions of markov processes. Inequalities 3, 1–8 (1972)
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Bahl, Brown, P.F., de Souza, P.V., Mercer, R.L.: Estimating hidden markov model parameters so as to maximize speech recognition accuracy. IEEE Trans. Speech Audio Process. 1, 77–83 (1993)
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Hernando, J. (2015). Hidden Markov Models. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_195
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